



'''simple docstring'''



import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()
def        UpperCAmelCase (    A	:					Tuple       ,		A	:					int       ,		A	:					Union[str, Any]					):
							# Construct model
							if gpta_config_file == "":
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      GPTaConfig()
							else:
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      GPTaConfig.from_json_file(A					)
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      GPTaModel(A					)

							# Load weights from numpy
							load_tf_weights_in_gpta(A       ,		A       ,		A					)

							# Save pytorch-model
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      pytorch_dump_folder_path + '''/''' + CONFIG_NAME
							print(F"""Save PyTorch model to {pytorch_weights_dump_path}"""					)
							torch.save(model.state_dict()       ,		A					)
							print(F"""Save configuration file to {pytorch_config_dump_path}"""					)
							with open(A       ,		'''w'''       ,		encoding='''utf-8'''					) as f:
														f.write(config.to_json_string()					)


if __name__ == "__main__":
							lowerCAmelCase_  :							Any             =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--gpt2_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							parser.add_argument(
							    '--gpt2_config_file',
							    default='',
							    type=str,
							    help=(
							        'An optional config json file corresponding to the pre-trained OpenAI model. \n'
							        'This specifies the model architecture.'
							    ),
							)
							lowerCAmelCase_  :							Any             =    parser.parse_args()
							convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)









'''simple docstring'''



import os

lowerCAmelCase_  :							Union[str, Any]             =    {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}
def        UpperCAmelCase (    A	:					str					):
							SCREAMING_SNAKE_CASE       :   str         =      0

							SCREAMING_SNAKE_CASE       :   List[str]         =      0
							while index < len(A					) - 1:
														SCREAMING_SNAKE_CASE       :   Dict         =      SYMBOLS[numerals[index]]
														SCREAMING_SNAKE_CASE       :   str         =      SYMBOLS[numerals[index + 1]]
														if current_value < next_value:
																					total_value -= current_value
														else:
																					total_value += current_value
														index += 1
							total_value += SYMBOLS[numerals[index]]

							return total_value
def        UpperCAmelCase (    A	:					int					):
							SCREAMING_SNAKE_CASE       :   Any         =      ''''''

							SCREAMING_SNAKE_CASE       :   Dict         =      num // 1000
							numerals += m_count * "M"
							num %= 1000

							SCREAMING_SNAKE_CASE       :   str         =      num // 100
							if c_count == 9:
														numerals += "CM"
														c_count -= 9
							elif c_count == 4:
														numerals += "CD"
														c_count -= 4
							if c_count >= 5:
														numerals += "D"
														c_count -= 5
							numerals += c_count * "C"
							num %= 100

							SCREAMING_SNAKE_CASE       :   Any         =      num // 10
							if x_count == 9:
														numerals += "XC"
														x_count -= 9
							elif x_count == 4:
														numerals += "XL"
														x_count -= 4
							if x_count >= 5:
														numerals += "L"
														x_count -= 5
							numerals += x_count * "X"
							num %= 10

							if num == 9:
														numerals += "IX"
														num -= 9
							elif num == 4:
														numerals += "IV"
														num -= 4
							if num >= 5:
														numerals += "V"
														num -= 5
							numerals += num * "I"

							return numerals
def        UpperCAmelCase (    A	:					str = "/p089_roman.txt"					):
							SCREAMING_SNAKE_CASE       :   List[str]         =      0

							with open(os.path.dirname(A					) + roman_numerals_filename					) as filea:
														SCREAMING_SNAKE_CASE       :   str         =      filea.readlines()

							for line in lines:
														SCREAMING_SNAKE_CASE       :   Any         =      line.strip()
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      parse_roman_numerals(A					)
														SCREAMING_SNAKE_CASE       :   Any         =      generate_roman_numerals(A					)
														savings += len(A					) - len(A					)

							return savings


if __name__ == "__main__":
							print(f'{solution() = }')




